Multimodal functional imaging and clinical correlates of pain regions in chronic low-back pain patients treated with spinal cord stimulation: a pilot study.

Objective: Spinal cord stimulation (SCS) is an invasive treatment option for patients suffering from chronic low-back pain (cLBP). It is an effective treatment that has been shown to reduce pain and increase the quality of life in patients. However, the activation of pain processing regions of cLBP patients receiving SCS has not been assessed using objective, quantitative functional imaging techniques. The purpose of the present study was to compare quantitative resting-state (rs)-fMRI and arterial spin labeling (ASL) measures between SCS patients and healthy controls and to correlate clinical measures with quantitative multimodal imaging indices in pain regions. Methods: Multi-delay 3D GRASE pseudo-continuous ASL and rs-fMRI data were acquired from five patients post-SCS with cLBP and five healthy controls. Three ASL measures and four rs-fMRI measures were derived and normalized into MNI space and smoothed. Averaged values for each measure from a pain atlas were extracted and compared between patients and controls. Clinical pain scores assessing intensity, sensitization, and catastrophizing, as well as others assessing global pain effects (sleep quality, disability, anxiety, and depression), were obtained in patients and correlated with pain regions using linear regression analysis. Results: Arterial transit time derived from ASL and several rs-fMRI measures were significantly different in patients in regions involved with sensation (primary somatosensory cortex and ventral posterolateral thalamus [VPL]), pain input (posterior short gyrus of the insula [PS]), cognition (dorsolateral prefrontal cortex [DLPC] and posterior cingulate cortex [PCC]), and fear/stress response (hippocampus and hypothalamus). Unidimensional pain rating and sensitization scores were linearly associated with PS, VPL, DLPC, PCC, and/or amygdala activity in cLBP patients. Conclusion: The present results provide evidence that ASL and rs-fMRI can contrast functional activation in pain regions of cLBP patients receiving SCS and healthy subjects, and they can be associated with clinical pain evaluations as quantitative assessment tools.

Feasibility of submillimeter functional quantitative susceptibility mapping using 3D echo planar imaging at 7 T.

Quantitative susceptibility mapping (QSM) is a tool for mapping tissue susceptibility. Using QSM for functional brain mapping, it is possible to directly quantify blood-oxygen-level-dependent (BOLD) susceptibility changes. This study presents a submillimeter functional QSM (fQSM) approach compared to BOLD fMRI from data acquired with 3D gradient-echo echo planar imaging (EPI) at ultra-high field. Complex EPI data were acquired in nine healthy subjects with varying temporal and spatial resolutions and used for BOLD fMRI and for fQSM. Right-hand finger tapping experiments were performed as well as one measurement with intentional subject movement. Susceptibility maps were computed using 3D path-based unwrapping, the variable-kernel sophisticated harmonic artifact reduction for phase data, and the streaking artifact reduction for QSM algorithm. Functional data analysis included general linear modeling and computation of z-scores. Submillimeter data were denoised using NOise reduction with DIstribution Corrected (NORDIC), which improved z-scores in the motor cortex for fQSM and fMRI. An expected increase in BOLD fMRI signal and corresponding decrease in magnetic susceptibility was observed in sensorimotor areas during active periods. For all experiments, fQSM showed smaller activation regions compared with fMRI. The percentage of high negative t-values localized in the cortex was higher for fQSM (52%) than for positive or negative t-values for fMRI (45%). For the scans with intentional motion, movement exceeded the size of a voxel, but paradigm dependent signal evolution could be recovered using motion correction. In conclusion, this study demonstrates the feasibility of submillimeter whole-brain fQSM with voxel volume of 0.53 µL. In comparison to traditional BOLD fMRI, fQSM provided improved localization of brain activation within the cortex, especially in submillimeter 3D EPI sequences.

Neural correlates of systemic lidocaine administration in healthy adults measured by functional MRI: a single arm open label study.

INTRODUCTION: Intravenous lidocaine is increasingly used as a nonopioid analgesic, but how it acts in the brain is incompletely understood. We conducted a functional MRI study of pain response, resting connectivity, and cognitive task performance in volunteers to elucidate the effects of lidocaine at the brain-systems level. METHODS: We enrolled 27 adults (age 22-55 yr) in this single-arm, open-label study. Pain response task and resting-state functional MRI scans at 3 T were obtained at baseline and then with a constant effect-site concentration of lidocaine. Electric nerve stimulation, titrated in advance to 7/10 intensity, was used for the pain task (five times every 10 s). Group-level differences in pain task-evoked responses (primary outcome, focused on the insula) and in resting connectivity were compared between baseline and lidocaine conditions, using adjusted P<0.05 to account for multiple comparisons. Pain ratings and performance on a brief battery of computer-based tasks were also recorded. RESULTS: Lidocaine infusion was associated with decreased pain-evoked responses in the insula (left: Z=3.6, P<0.001, right: Z=3.6, P=0.004) and other brain areas including the cingulate gyrus, thalamus, and primary sensory cortex. Resting-state connectivity showed significant diffuse reductions in both region-to-region and global connectivity measures with lidocaine. Small decreases in pain intensity and unpleasantness and worse memory performance were also seen with lidocaine. CONCLUSIONS: Lidocaine was associated with broad reductions in functional MRI response to acute pain and modulated whole-brain functional connectivity, predominantly decreasing long-range connectivity. This was accompanied by small but significant decreases in pain perception and memory performance. CLINICAL TRIAL REGISTRATION: NCT05501600.

Molecular mechanisms underlying the neural correlates of working memory.

BACKGROUND: Working memory (WM), a core component of executive functions, relies on a dedicated brain system that maintains and stores information in the short term. While extensive neuroimaging research has identified a distributed set of neural substrates relevant to WM, their underlying molecular mechanisms remain enigmatic. This study investigated the neural correlates of WM as well as their underlying molecular mechanisms. RESULTS: Our voxel-wise analyses of resting-state functional MRI data from 502 healthy young adults showed that better WM performance (higher accuracy and shorter reaction time of the 3-back task) was associated with lower functional connectivity density (FCD) in the left inferior temporal gyrus and higher FCD in the left anterior cingulate cortex. A combination of transcriptome-neuroimaging spatial correlation and the ensemble-based gene category enrichment analysis revealed that the identified neural correlates of WM were associated with expression of diverse gene categories involving important cortical components and their biological processes as well as sodium channels. Cross-region spatial correlation analyses demonstrated significant associations between the neural correlates of WM and a range of neurotransmitters including dopamine, glutamate, serotonin, and acetylcholine. CONCLUSIONS: These findings may help to shed light on the molecular mechanisms underlying the neural correlates of WM.

Amygdala self-neuromodulation capacity as a window for process-related network recruitment.

Neurofeedback (NF) has emerged as a promising avenue for demonstrating process-related neuroplasticity, enabling self-regulation of brain function. NF targeting the amygdala has drawn attention to therapeutic potential in psychiatry, by potentially harnessing emotion-regulation processes. However, not all individuals respond equally to NF training, possibly owing to varying self-regulation abilities. This underscores the importance of understanding the mechanisms behind successful neuromodulation (i.e. capacity). This study aimed to investigate the establishment and neural correlates of neuromodulation capacity using data from repeated sessions of amygdala electrical fingerprint (Amyg-EFP)-NF and post-training functional magnetic resonance imaging (fMRI)-NF sessions. Results from 97 participants (healthy controls and post-traumatic stress disorder and fibromyalgia patients) revealed increased Amyg-EFP neuromodulation capacity over training, associated with post-training amygdala-fMRI modulation capacity and improvements in alexithymia. Individual differenaces in this capacity were associated with pre-training amygdala reactivity and initial neuromodulation success. Additionally, amygdala downregulation during fMRI-NF co-modulated with other regions such as the posterior insula and parahippocampal gyrus. This combined modulation better explained EFP-modulation capacity and improvement in alexithymia than the amygdala modulation alone, suggesting the relevance of this broader network to gained capacity. These findings support a network-based approach for NF and highlight the need to consider individual differences in brain function and modulation capacity to optimize NF interventions. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Functional connectivity across the human subcortical auditory system using an autoregressive matrix-Gaussian copula graphical model approach with partial correlations.

The auditory system comprises multiple subcortical brain structures that process and refine incoming acoustic signals along the primary auditory pathway. Due to technical limitations of imaging small structures deep inside the brain, most of our knowledge of the subcortical auditory system is based on research in animal models using invasive methodologies. Advances in ultrahigh-field functional magnetic resonance imaging (fMRI) acquisition have enabled novel noninvasive investigations of the human auditory subcortex, including fundamental features of auditory representation such as tonotopy and periodotopy. However, functional connectivity across subcortical networks is still underexplored in humans, with ongoing development of related methods. Traditionally, functional connectivity is estimated from fMRI data with full correlation matrices. However, partial correlations reveal the relationship between two regions after removing the effects of all other regions, reflecting more direct connectivity. Partial correlation analysis is particularly promising in the ascending auditory system, where sensory information is passed in an obligatory manner, from nucleus to nucleus up the primary auditory pathway, providing redundant but also increasingly abstract representations of auditory stimuli. While most existing methods for learning conditional dependency structures based on partial correlations assume independently and identically Gaussian distributed data, fMRI data exhibit significant deviations from Gaussianity as well as high-temporal autocorrelation. In this paper, we developed an autoregressive matrix-Gaussian copula graphical model (ARMGCGM) approach to estimate the partial correlations and thereby infer the functional connectivity patterns within the auditory system while appropriately accounting for autocorrelations between successive fMRI scans. Our results show strong positive partial correlations between successive structures in the primary auditory pathway on each side (left and right), including between auditory midbrain and thalamus, and between primary and associative auditory cortex. These results are highly stable when splitting the data in halves according to the acquisition schemes and computing partial correlations separately for each half of the data, as well as across cross-validation folds. In contrast, full correlation-based analysis identified a rich network of interconnectivity that was not specific to adjacent nodes along the pathway. Overall, our results demonstrate that unique functional connectivity patterns along the auditory pathway are recoverable using novel connectivity approaches and that our connectivity methods are reliable across multiple acquisitions.

Altered Amplitude of Low-Frequency Fluctuations of rs-fMRI Signal followed by rTMS Analgesic Effects in Non-Specific Chronic Low Back Pain (CLBP) Patients.

Background: Non-specific chronic low back pain (CLBP) is a common painful condition and is responsible for different physical disorders. Despite alternative therapies, patients still suffer from persistent pain. Repetitive transcranial magnetic stimulation (rTMS) has provided much evidence of pain reduction, but results have not been examined deeply in CLBP symptoms. Objective: The analgesic effect of rTMS in non-specific CLBP patients was evaluated by the amplitude of low-frequency fluctuation (ALFF) analysis in resting-state fMRI. Material and Methods: In this experimental study, fifteen non-specific CLBP participants (46.87±10.89 years) received 20 Hz rTMS over the motor cortex. The pain intensity and brain functional scan were obtained during pre and post-stimulation for all participants. The ALFF maps of the brain in two scan sessions were identified and the percentage of pain reduction (PPR%) was determined using paired t-test. Also, correlation analysis was used to find a relationship between ALFFs and pain intensity. Results: Pain intensity was significantly reduced after induced-rTMS in non-specific CLBP (36.22%±13.28, P<0.05). Positive correlation was found between ALFF in the insula (INS) and pain intensity (rpre-rTMS=0.59, rpost-rTMS=0.58) while ALFF in medial prefrontal cortex (mPFC) and pain intensity had negatively correlated (rpre-rTMS=-0.54, rpost-rTMS=-0.56) (P<0.05). ALFF increased in mPFC while INS, thalamus (THA), and supplementary motor area (SMA) showed decremental ALFF followed by rTMS. Conclusion: This study demonstrated that ALFF in INS, THA, mPFC, and SMA is associated with CLBP symptoms and analgesic effects of rTMS. ALFF potentially seems to be a proper objective neuroimaging parameter to link spontaneous brain activity with pain intensity in non-specific CLBP patients.

Corrigendum: Neurophysiological hallmarks of Huntington's disease progression: an EEG and fMRI connectivity study.

[This corrects the article DOI: 10.3389/fnagi.2023.1270226.].

The mesolimbic system and the loss of higher order network features in schizophrenia when learning without reward.

Introduction: Schizophrenia is characterized by a loss of network features between cognition and reward sub-circuits (notably involving the mesolimbic system), and this loss may explain deficits in learning and cognition. Learning in schizophrenia has typically been studied with tasks that include reward related contingencies, but recent theoretical models have argued that a loss of network features should be seen even when learning without reward. We tested this model using a learning paradigm that required participants to learn without reward or feedback. We used a novel method for capturing higher order network features, to demonstrate that the mesolimbic system is heavily implicated in the loss of network features in schizophrenia, even when learning without reward. Methods: fMRI data (Siemens Verio 3T) were acquired in a group of schizophrenia patients and controls (n=78; 46 SCZ, 18 ≤ Age ≤ 50) while participants engaged in associative learning without reward-related contingencies. The task was divided into task-active conditions for encoding (of associations) and cued-retrieval (where the cue was to be used to retrieve the associated memoranda). No feedback was provided during retrieval. From the fMRI time series data, network features were defined as follows: First, for each condition of the task, we estimated 2nd order undirected functional connectivity for each participant (uFC, based on zero lag correlations between all pairs of regions). These conventional 2nd order features represent the task/condition evoked synchronization of activity between pairs of brain regions. Next, in each of the patient and control groups, the statistical relationship between all possible pairs of 2nd order features were computed. These higher order features represent the consistency between all possible pairs of 2nd order features in that group and embed within them the contributions of individual regions to such group structure. Results: From the identified inter-group differences (SCZ ≠ HC) in higher order features, we quantified the respective contributions of individual brain regions. Two principal effects emerged: 1) SCZ were characterized by a massive loss of higher order features during multiple task conditions (encoding and retrieval of associations). 2) Nodes in the mesolimbic system were over-represented in the loss of higher order features in SCZ, and notably so during retrieval. Discussion: Our analytical goals were linked to a recent circuit-based integrative model which argued that synergy between learning and reward circuits is lost in schizophrenia. The model's notable prediction was that such a loss would be observed even when patients learned without reward. Our results provide substantial support for these predictions where we observed a loss of network features between the brain's sub-circuits for a) learning (including the hippocampus and prefrontal cortex) and b) reward processing (specifically constituents of the mesolimbic system that included the ventral tegmental area and the nucleus accumbens. Our findings motivate a renewed appraisal of the relationship between reward and cognition in schizophrenia and we discuss their relevance for putative behavioral interventions.

Principles of intensive human neuroimaging.

The rise of large, publicly shared functional magnetic resonance imaging (fMRI) data sets in human neuroscience has focused on acquiring either a few hours of data on many individuals ('wide' fMRI) or many hours of data on a few individuals ('deep' fMRI). In this opinion article, we highlight an emerging approach within deep fMRI, which we refer to as 'intensive' fMRI: one that strives for extensive sampling of cognitive phenomena to support computational modeling and detailed investigation of brain function at the single voxel level. We discuss the fundamental principles, trade-offs, and practical considerations of intensive fMRI. We also emphasize that intensive fMRI does not simply mean collecting more data: it requires careful design of experiments to enable a rich hypothesis space, optimizing data quality, and strategically curating public resources to maximize community impact.

Preventive treatment effects on brain structures and functions in patients with chronic migraine: A multimodel magnetic resonance imaging study.

Patients with chronic migraine (CM) often exhibit structural and functional alterations in pain-matrix regions, but it remains unclear how preventive treatment affects these changes. Therefore, this study aimed to investigate the structural and functional changes in pain-matrix regions in CM patients after 6-month treatment. A total of 24 patients with CM and 15 healthy controls were recruited for this study. Patients were divided into responder group (N = 9) and non-responder group (N = 15). After completing the Migraine Disability Assessment (MIDAS) questionnaire, all patients underwent whole-brain high-resolution T1-weighted images, diffusion-weighted imaging, and resting-state functional magnetic resonance imaging at baseline and 6-month follow-up. Whole brain gray matter volume and white matter diffusion indices were analyzed using voxel-based analysis. Structural and functional connectivity analyses were performed to understand brain changes in patients after 6-month preventive treatment. The responder group exhibited significantly higher MIDAS scores than the non-responder group at baseline, but no significant difference between the two groups at follow-up. No significant interval change was noted in gray matter volume, white matter diffusion indices, and structural connectivity in CM patients after 6-month treatment. Nonetheless, the functional connectivity was significantly increased between occipital, temporal lobes and cerebellum, and was significantly decreased between parietal and temporal lobes after 6-month preventive treatment. We concluded that resting-state functional connectivity was suitable for investigating the preventive treatment effect on CM patients.

Optimization and validation of multi-echo, multi-contrast SAGE acquisition in fMRI.

The purpose of this study was to optimize and validate a multi-contrast, multi-echo fMRI method using a combined spin- and gradient-echo (SAGE) acquisition. It was hypothesized that SAGE-based blood oxygen level-dependent (BOLD) functional MRI (fMRI) will improve sensitivity and spatial specificity while reducing signal dropout. SAGE-fMRI data were acquired with five echoes (2 gradient-echoes, 2 asymmetric spin-echoes, and 1 spin-echo) across 12 protocols with varying acceleration factors, and temporal SNR (tSNR) was assessed. The optimized protocol was then implemented in working memory and vision tasks in 15 healthy subjects. Task-based analysis was performed using individual echoes, quantitative dynamic relaxation times T2 * and T2, and echo time-dependent weighted combinations of dynamic signals. These methods were compared to determine the optimal analysis method for SAGE-fMRI. Implementation of a multiband factor of 2 and sensitivity encoding (SENSE) factor of 2.5 yielded adequate spatiotemporal resolution while minimizing artifacts and loss in tSNR. Higher BOLD contrast-to-noise ratio (CNR) and tSNR were observed for SAGE-fMRI relative to single-echo fMRI, especially in regions with large susceptibility effects and for T2-dominant analyses. Using a working memory task, the extent of activation was highest with T2 *-weighting, while smaller clusters were observed with quantitative T2 * and T2. SAGE-fMRI couples the high BOLD sensitivity from multi-gradient-echo acquisitions with improved spatial localization from spin-echo acquisitions, providing two contrasts for analysis. SAGE-fMRI provides substantial advantages, including improving CNR and tSNR for more accurate analysis.

Functional connectivity in resting-state networks relates to short-term global cognitive functioning in cardiac arrest survivors.

Long-term cognitive impairment is common in cardiac arrest survivors. Screening to identify patients at risk is recommended. Functional magnetic resonance brain imaging (fMRI) holds potential to contribute to prediction of cognitive outcomes. In this study, we investigated the possible value of early changes in resting-state networks for predicting short and long-term cognitive functioning of cardiac arrest survivors. We performed a prospective multicenter cohort study in cardiac arrest survivors in three Dutch hospitals. Resting-state fMRI scans were acquired within a month after cardiac arrest. We primarily focused on functional connectivity within the default-mode network (DMN) and salience network (SN), and additionally explored functional connectivity in seven other networks. Cognitive outcome was measured using the Montreal Cognitive Assessment (MoCA) during hospital admission and at 3 and 12 months, and by neuropsychological examination (NPE) at 12 months. We tested mixed effects models to evaluate the value of connectivity within the networks for predicting global cognitive outcomes at the three time points, and long-term cognitive outcomes in the memory, attention, and executive functioning domains. We included 80 patients (age 60 ± 11 years, 72 (90%) male). MoCA scores increased significantly between hospital admission and 3 months (ΔMoCAhospital-3M = 2.89, p < 0.01), but not between 3 and 12 months (ΔMoCA3M-12M = 0.38, p = 0.52). Connectivity within the DMN, SN, and dorsal attention network (DAN) was positively related to global cognitive functioning during hospital admission (ßDMN = 0.85, p = 0.03; ßSN = 1.48, p < 0.01; ßDAN = 0.96, p = 0.01), but not at 3 and 12 months. Network connectivity was also unrelated to long-term memory, attention, or executive functioning. Resting-state functional connectivity in the DMN, SN, and DAN measured in the first month after cardiac arrest is related to short-term global, but not long-term global or domain-specific cognitive performance of survivors. These results do not support the value of functional connectivity within these RSNs for prediction of long-term cognitive performance after cardiac arrest.

Attention-Enhanced Fusion of Structural and Functional MRI for Analyzing HIV-Associated Asymptomatic Neurocognitive Impairment.

Asymptomatic neurocognitive impairment (ANI) is a predominant form of cognitive impairment among individuals infected with human immunodeficiency virus (HIV). The current diagnostic criteria for ANI primarily rely on subjective clinical assessments, possibly leading to different interpretations among clinicians. Some recent studies leverage structural or functional MRI containing objective biomarkers for ANI analysis, offering clinicians companion diagnostic tools. However, they mainly utilize a single imaging modality, neglecting complementary information provided by structural and functional MRI. To this end, we propose an attention-enhanced structural and functional MRI fusion (ASFF) framework for HIV-associated ANI analysis. Specifically, the ASFF first extracts data-driven and human-engineered features from structural MRI, and also captures functional MRI features via a graph isomorphism network and Transformer. A mutual cross-attention fusion module is then designed to model the underlying relationship between structural and functional MRI. Additionally, a semantic inter-modality constraint is introduced to encourage consistency of multimodal features, facilitating effective feature fusion. Experimental results on 137 subjects from an HIV-associated ANI dataset with T1-weighted MRI and resting-state functional MRI show the effectiveness of our ASFF in ANI identification. Furthermore, our method can identify both modality-shared and modality-specific brain regions, which may advance our understanding of the structural and functional pathology underlying ANI.

Repetitive Transcranial Magnetic Stimulation (rTMS) Treatment Reduces Variability in Brain Function in Schizophrenia: Data From a Double-Blind, Randomized, Sham-Controlled Trial.

BACKGROUND/HYPOTHESIS: There is increasing awareness of interindividual variability in brain function, with potentially major implications for repetitive transcranial magnetic stimulation (rTMS) efficacy. We perform a secondary analysis using data from a double-blind randomized controlled 4-week trial of 20 Hz active versus sham rTMS to dorsolateral prefrontal cortex (DLPFC) during a working memory task in participants with schizophrenia. We hypothesized that rTMS would change local functional activity and variability in the active group compared with sham. STUDY DESIGN: 83 participants were randomized in the original trial, and offered neuroimaging pre- and post-treatment. Of those who successfully completed both scans (n = 57), rigorous quality control left n = 42 (active/sham: n = 19/23), who were included in this analysis. Working memory-evoked activity during an N-Back (3-Back vs 1-Back) task was contrasted. Changes in local brain activity were examined from an 8 mm ROI around the rTMS coordinates. Individual variability was examined as the mean correlational distance (MCD) in brain activity pattern from each participant to others within the same group. RESULTS: We observed an increase in task-evoked left DLPFC activity in the active group compared with sham (F1,36 = 5.83, False Discovery Rate (FDR))-corrected P = .04). Although whole-brain activation patterns were similar in both groups, active rTMS reduced the MCD in activation pattern compared with sham (F1,36 = 32.57, P < .0001). Reduction in MCD was associated with improvements in attention performance (F1,16 = 14.82, P = .0014, uncorrected). CONCLUSIONS: Active rTMS to DLPFC reduces individual variability of brain function in people with schizophrenia. Given that individual variability is typically higher in schizophrenia patients compared with controls, such reduction may "normalize" brain function during higher-order cognitive processing.

Integrated Brain Connectivity Analysis with fMRI, DTI, and sMRI Powered by Interpretable Graph Neural Networks.

Multimodal neuroimaging modeling has become a widely used approach but confronts considerable challenges due to heterogeneity, which encompasses variability in data types, scales, and formats across modalities. This variability necessitates the deployment of advanced computational methods to integrate and interpret these diverse datasets within a cohesive analytical framework. In our research, we amalgamate functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and structural MRI (sMRI) into a cohesive framework. This integration capitalizes on the unique strengths of each modality and their inherent interconnections, aiming for a comprehensive understanding of the brain's connectivity and anatomical characteristics. Utilizing the Glasser atlas for parcellation, we integrate imaging-derived features from various modalities-functional connectivity from fMRI, structural connectivity from DTI, and anatomical features from sMRI-within consistent regions. Our approach incorporates a masking strategy to differentially weight neural connections, thereby facilitating a holistic amalgamation of multimodal imaging data. This technique enhances interpretability at connectivity level, transcending traditional analyses centered on singular regional attributes. The model is applied to the Human Connectome Project's Development study to elucidate the associations between multimodal imaging and cognitive functions throughout youth. The analysis demonstrates improved predictive accuracy and uncovers crucial anatomical features and essential neural connections, deepening our understanding of brain structure and function. This study not only advances multi-modal neuroimaging analytics by offering a novel method for the integrated analysis of diverse imaging modalities but also improves the understanding of intricate relationship between the brain's structural and functional networks and cognitive development.

Coupled pulsatile vascular and paravascular fluid dynamics in the human brain.

BACKGROUND: Cardiac pulsation propels blood through the cerebrovascular network to maintain cerebral homeostasis. The cerebrovascular network is uniquely surrounded by paravascular cerebrospinal fluid (pCSF), which plays a crucial role in waste removal, and its flow is suspected to be driven by arterial pulsations. Despite its importance, the relationship between vascular and paravascular fluid dynamics throughout the cardiac cycle remains poorly understood in humans. METHODS: In this study, we developed a non-invasive neuroimaging approach to investigate the coupling between pulsatile vascular and pCSF dynamics within the subarachnoid space of the human brain. Resting-state functional MRI (fMRI) and dynamic diffusion-weighted imaging (dynDWI) were retrospectively cardiac-aligned to represent cerebral hemodynamics and pCSF motion, respectively. We measured the time between peaks (∆TTP) in d d ϕ f M R I and dynDWI waveforms and measured their coupling by calculating the waveforms correlation after peak alignment (correlation at aligned peaks). We compared the ∆TTP and correlation at aligned peaks between younger [mean age: 27.9 (3.3) years, n = 9] and older adults [mean age: 70.5 (6.6) years, n = 20], and assessed their reproducibility within subjects and across different imaging protocols. RESULTS: Hemodynamic changes consistently precede pCSF motion. ∆TTP was significantly shorter in younger adults compared to older adults (-0.015 vs. -0.069, p < 0.05). The correlation at aligned peaks were high and did not differ between younger and older adults (0.833 vs. 0.776, p = 0.153). The ∆TTP and correlation at aligned peaks were robust across fMRI protocols (∆TTP: -0.15 vs. -0.053, p = 0.239; correlation at aligned peaks: 0.813 vs. 0.812, p = 0.985) and demonstrated good to excellent within-subject reproducibility (∆TTP: intraclass correlation coefficient = 0.36; correlation at aligned peaks: intraclass correlation coefficient = 0.89). CONCLUSION: This study proposes a non-invasive technique to evaluate vascular and paravascular fluid dynamics. Our findings reveal a consistent and robust cardiac pulsation-driven coupling between cerebral hemodynamics and pCSF dynamics in both younger and older adults.

The Contribution of Cognitive Control Networks in Word Selection Processing in Parkinson's Disease: Novel Insights from a Functional Connectivity Study.

Parkinson's disease (PD) patients are impaired in word production when the word has to be selected among competing alternatives requiring higher attentional resources. In PD, word selection processes are correlated with the structural integrity of the inferior frontal gyrus, which is critical for response selection, and the uncinate fasciculus, which is necessary for processing lexical information. In early PD, we investigated the role of the main cognitive large-scale networks, namely the salience network (SN), the central executive networks (CENs), and the default mode network (DMN), in word selection. Eighteen PD patients and sixteen healthy controls were required to derive nouns from verbs or generate verbs from nouns. Participants also underwent a resting-state functional MRI. Functional connectivity (FC) was examined using independent component analysis. Functional seeds for the SN, CENs, and DMN were defined as spheres, centered at the local activation maximum. Correlations were calculated between the FC of each functional seed and word production. A significant association between SN connectivity and task performance and, with less evidence, between CEN connectivity and the task requiring selection among a larger number of competitors, emerged in the PD group. These findings suggest the involvement of the SN and CEN in word selection in early PD, supporting the hypothesis of impaired executive control.

The Role of Neuropsychology in Neurosurgical Care: A Review of the Literature.

In this review article, the authors describe the invaluable role that neuropsychology plays in neurosurgical care for a broad range of pathologies. As our understanding of cognitive and behavioral implications of diseases and surgical management of the brain has deepened, so has the need to preserve the quality of life for patients undergoing surgery to optimize well-being and overall survival. This article recounts the history of neuropsychology, details tools and techniques used by neuropsychologists including the neuropsychological assessment, fMRI, tractography, and awake surgery, and discusses the practical applications of neuropsychological evaluation in tumor surgery, epilepsy, deep brain modulation, and beyond.

Functional brain alterations in COVID-19 patients using resting-state fMRI: a systematic review.

This study systematically reviews the available evidence on resting-state functional magnetic resonance imaging (rs-fMRI) related to neurological symptoms and cognitive declines in COVID-19 patients. We followed PRISMA guidelines and looked up the PubMed, and Scopus databases for articles search on COVID-19 patients with neurological impairments, and functional connectivity alteration using rs-fMRI technique. Articles published between January 1, 2020, and May 31, 2024, are included in this study. The Quality Assessment Tool for Observational Prospective and Cross-Sectional Studies from the National Heart, Lung, and Blood Institute (NHLBI) was used to assess the quality of papers. A total of 15 articles met the inclusion criteria. The result reveals that the most prevalent neurological impairment associated with COVID-19 was cognitive decline, encompassing issues in attention, memory, processing speed, executive functions, language, and visuospatial ability. The brain connectivity results reveal that two brain areas were functionally altered; the prefrontal cortex and parahippocampus. The functional connectivity mainly increased in the frontal, temporal, and anterior piriform cortex, and reduced in the cerebellum, superior orbitofrontal cortex, and middle temporal gyrus, which also correlated with cognitive decline. The findings of neurological symptoms indicate one study reported a Disorder of Consciousness (DoC), and four studies reported COVID-19 patients with olfactory dysfunction. The present study concludes that COVID-19 can alter brain functional connectivity and offers significant insight into how COVID-19 affects the neuronal foundation of cognitive decline and other neurological impairments.